Flywheel discovery system that twins machine learning with high-throughput expression and laboratory analysis to identify and develop individual proteins as food ingredients

ABSTRACT

This disclosure provides a technology for developing alternative protein sources for use in industrial food production. The technology mines sequence data by a process that is done partly in silico. Instead of sampling and testing a vast library of compounds, machine learning and implementation narrows the field of functional candidates by predictive modeling based on known protein structure. Candidate proteins that are selected by this analysis are then produced and screened in a high-throughput manner by recombinant expression and testing to determine whether they have a target function. Multiple cycles of the machine learning, database mining, expression, and testing are done to yield potential ingredients suitable for assessment as part of a commercial food product.

REFERENCE TO RELATED APPLICATIONS

This patent disclosure is a continuation of U.S. patent application Ser.No. 17/520,201, which is set to issue on Sep. 13, 2022 as U.S. Pat. No.11,439,159, which claims the priority benefit of U.S. provisional patentapplication 63/163,949, filed Mar. 22, 2021. The priority applicationsare hereby incorporated herein by reference in their entireties for allpurposes.

FIELD OF THE INVENTION

The technology disclosed and claimed below relates generally to theidentification of individual proteins as new food ingredients. Itcombines the fields of computer prediction and learning of structuraland functional characteristics of biomolecules in a sequence database,rapid-throughput production of previously uncharacterized proteins, andassays related to physicochemical and sensory characteristics ofproteins that are desirable for food products.

BACKGROUND

Agriculture has an enormous environmental footprint, playing asignificant role in causing climate change, water scarcity, airpollution, land degradation, and deforestation. The global food systemaccounts for about 37% of greenhouse gas emissions. Seven percent ofglobal freshwater is currently used for agriculture. By 2050, the globalpopulation is expected to grow to over 9.7 billion people. There is notenough clean water and arable land to meet increasing nutritionaldemands of the global population.

According to a recent authoritative report published by the World Bankand United Nations, continuing to feed the world's population at thispace until 2050 will clear most of the world's remaining forests,causing extinction of thousands of species, and releasing enoughgreenhouse gas emissions to exceed the 1.5° C. and 2° C. maximum warmingtargets in the Paris Agreement—even if emissions from all other humanactivities were eliminated. There is an urgent need to change currentapproaches to agriculture and food marketing to emphasize food productsthat are both sustainable and nutritious.

SUMMARY

This disclosure provides a technology for developing alternative proteinsources for use in industrial food production. Shim, Inc. has built athriving business from the idea that ingredients currently used incommercial food products can be substituted with proteins having knownstructure, but not previously known to have a desired target function.

For decades, the pharmaceutical industry has mined rich biologicallydiverse environments (tropical rainforest canopies and sea bottoms) todiscover natural but previously unidentified small molecules that workas antibiotics or have other therapeutic impact. The technologydescribed here is built on the same premise of mining naturalsources—except that the mining is done partly in silico.

Instead of sampling and testing a vast library of compounds from adistant or wide-ranging environment, this technology narrows the fieldof functional candidates by predictive functional modeling drawn fromknown protein structure. Protein candidates selected in this way can bescreened rapidly by recombinant expression and empirical testing todetermine whether they have a target function and are suitable forfurther development as food ingredients.

Some of the Features of the Technology Put Forth in this Disclosure

This disclosure provides (among other things) a discovery method foridentifying and developing proteins for use in manufacture of a combinedproduct.

First, a computer system that is adapted for machine learning is trainedto group similar proteins together and/or predict whether a protein hasa preselected target function, wherein the target function is chosenbased on the field of endeavor of the project. The ability of aparticular protein to perform a desired target function may be predictedby the computer from one or more structural and/or functionalcharacteristics of the protein, including at least the protein's aminoacid sequence. Additional structural characteristics may includethree-dimensional protein structure obtained from crystallography data,or predicted from the protein's amino acid sequence. Other functionalcharacteristics may include molecular weight, charge, isoelectric point,solubility in aqueous solution, hydrophobicity, and binding affinity forother proteins or protein classes.

The computer system is trained by a process of machine learning thatcomprises inputting into the computer system a training data set thatcontains said characteristics for a plurality of proteins known to havethe target function, and that also contains said characteristics for aplurality of proteins known not to have the target function.

Following the training, the computer system is applied to a source dataset (such as a database consisting of or containing likely candidates).The database may contain mostly “naturally occurring” proteins, whichmeans proteins that can be identified in biological sources in nature,or can be isolated or otherwise obtained from biological sources withoutrecombinant DNA technology. The database includes structural and othercharacteristics for each protein it contains, including at least eachprotein's amino acid sequence.

The trained computer system assesses proteins in the database, andcompiles a list that identifies or ranks protein candidates that arepredicted (but typically not already known) to have the target function.Characteristics analyzed in the training in step and/or included inpredicting target function may include a homolog comparison forsimilarity of one or more of the following structural features in anycombination: protein amino acid sequence, protein three-dimensionalstructure (obtained from crystallography data or predicted from theprotein's amino acid sequence), vector representations ofphysicochemical and biochemical properties of amino acids and/orclusters of amino acids in each protein, optionally combined with vectorrepresentations of properties of the protein as a whole.

Empirical evaluation is done next. The protein candidates on thecomputer-generated list are recombinantly expressed and purified in ahigh throughput manner. This can include expressing each protein with atag, and using the tag for affinity purification using a conjugatebinding partner. The isolated proteins are then assayed to determine orquantify which of the expressed protein candidates actually have thetarget function. The expressing and purifying may be repeated one ormore times to improve volume and/or quality of protein production. Theexpressing, purifying, and assaying is generally done in a manner thatpromotes high-throughput screening. Besides the ability of expressedprotein to perform the target function, the empirical evaluation mayinclude determining or measuring other features, such as physicochemicalproperties selected from thermal stability, buffering capacity,solubility, and charge.

One or more of the expressed protein candidates that are determined tohave the target function above a certain threshold or at a satisfactorylevel are then selected for further workup. This would includeadditional tests to determine whether the protein meets desiredperformance requirements when placed in the context of its intendedpurpose. For industrial production, the protein may be isolated from anatural or agricultural source, or produced recombinantly in a differentsystem than the process used for high-throughput evaluation.

The computer prediction and empirical screening can be done in aniterative or cyclical fashion, wherein the structural data and/or assayresults for the protein candidates that have been tested are added intothe training data set. One, two, or more than two additional cycles ofthe predicting, expressing, and testing can be done until a desirednumber of potential food ingredients for the intended use have beenselected. If the number of potential ingredients obtained in a singlepass-through of the predicting, expressing, and testing is sufficientfor the user's purposes, then additional iterations are optional. Oncethe number of potential ingredients for the intended purpose has beenobtained, each protein is typically manufactured in its intended contextor a proxy thereof to determine whether it meets desired performancerequirements.

Depending on the field of use and objectives of the user, the technologycan optionally be implemented without machine learning and/or withoutreiteration. In some contexts, technology can also be implementedwithout using homology comparison of primary amino acid sequence data asthe primary focus. Instead, the comparison is done by comparing proteinsin a database with proteins known to have a target function usingthree-dimensional protein structure, and/or vector representation ofstructural and three-dimensional features of individual amino acids andclusters thereof. This helps identify candidates that may have thetarget function because of a shared core structure and even if theydon't share primary sequence homology with proteins known to have thetarget function.

For example, potential food ingredients from natural sources can beidentified by accessing a database that contains amino acid sequencedata of naturally occurring proteins. A plurality of the proteins in thedatabase are encoded as a vector representation of physicochemical andbiochemical properties of amino acids and clusters of amino acids(typically using artificial intelligence in an appropriately programmedcomputer in combination with input from the user). The vectorrepresentations of proteins in the database are then compared withvector representations of proteins known to have a desired targetfunction. Alternatively or in addition, known or predictedthree-dimensional structure of proteins in the database are comparedwith proteins known to have the desired target function. Naturallyoccurring proteins in the database are thereby identified and/or rankedaccording to whether they are predicted to have the target function,thereby obtaining a set of protein candidates.

The candidates are recombinantly expressed and purified, and then assaysare conducted to determine or quantify which of the protein candidatesactually have the target function. Based on the assay results, one ormore of the expressed protein candidates may be characterized aspotential food ingredients. The potential ingredients are then tested todetermine whether they meet desired performance requirements as part ofa food preparation. Optionally, the data obtained from the assays isused to adjust the encoding and/or weights of feature vectors ofproteins in the database into vector representations, and the steps ofcomparing, expressing, and assaying are reiterated to obtain additionalpotential food ingredients. Optionally, the encoding of the proteins isrepeated and optimized by the assistance of machine learning and/or userinput.

The various procedures and steps of the discovery system need not bedone in a particular order unless explicitly stated or otherwiserequired. Often, results of the empirical evaluation will be used tohelp train the computer system on an ongoing basis, and the computersystem will continue to mine databases in an ongoing manner to nominateadditional proteins to the list of proteins predicted to have the targetfunction.

Applications of the Technology

These discovery methods of computer prediction, expression, andscreening can be used for identifying ingredients for food preparationshaving a desired property, for the purposes of introducing the propertyinto the foods, or substituting or supplementing for another protein(potentially from an animal source) that is more traditionally used insuch foods. The same discovery methods can also be applied to thediscovery and development of proteins for use in other fields ofmanufacture, as described in the description that follows.

Presence of species homologs in a protein database may skew the list ofprotein candidates selected by the computer in favor of protein classeshaving a relatively large number of species homologs in preference toother protein candidates. For purposes of compiling an initial list, theuser may decide to remove or downgrade proteins identified as specieshomologs and/or isoforms from the set of protein candidates, either in asupervised or unsupervised manner. Subsequently, for purposes ofselection refinement, the user may decide to focus the computerselection criteria on homologs of a protein that has been evaluatedempirically as having promise for further development, therebyoptimizing the choice of which homolog should be used for ultimateworkup.

In some instances, a function that is predicted to be present in aprotein by computer analysis may not be evident in empirical testing.This means that the function is potentially present but “masked”(hidden) within the protein stoichiometrically or by other means. Inthis situation, development, assessment, and ultimate selection of aprotein candidate may include unmasking the target function. Theunmaking may be done by recombinantly expressing and purifying apotentially unmasked version of the protein in which a part of theprotein predicted to have the target function is excised from otherparts of the protein that are believed to mask the target function, andthen conducting additional assays to determine or measure whether thepotentially unmasked version of the protein has the target function. Theprotein expressed for testing or ultimately selected for the intendedpropose may be a truncated version of the naturally occurring protein,or a fusion protein containing the naturally occurring protein or atruncated version thereof.

The discovery method may also include selecting proteins in the computerprediction phase, or selecting promising candidates following empiricalassessment based on other desirable features beyond ability of theprotein to perform the target function. Positive selection criteria mayinclude solubility, ease of expression, ease of purification, stabilityon storage, and mixability. Negative selection criteria may includepotential toxicity and adverse environmental effects. Such criteria maybe predicted by computer algorithm in the process of candidate ranking,and/or determined in the empirical evaluation, in any combination.

The discovery system of this disclosure may be put to use to identifypotential food ingredients for any suitable purpose. Reasons for usingthis system may include replacing an animal or unsustainable sourced ofa food ingredient with a suitable substitute, or to confer or augment aparticular function or property to improve a food product.

In this context, the target function may be selected from antimicrobialactivity, gelation, moisture retention, fat structuring, adhesion, fiberformation, particular flavors, and other functions referred to below.Additional positive selection criteria may include one or more desirableflavors or sensory properties, such as texture and mouth-feel. Negativeselection criteria may include allergenicity or immunogenicity,incompatibility with other food ingredients, an adverse physiologicaleffect, and an undesirable flavor. The empirical evaluation may includeproperties such as emulsion stability, foam stability, gelation,chewiness, storage modulus, water binding capacity, swell ratio inwater, sedimentation rate, adhesiveness, antimicrobial activity, andenzyme activity.

Performance requirements of potential food ingredients used in theultimate workup may include sufficient activity of the target functionby the potential food ingredient when compounded into a food product,and compliance of the food product with regulatory requirements.

This disclosure provides a method of preparing a food product containinga protein not previously used as a food ingredient, selected andevaluated by the discovery system put forth above. A conventional foodingredient may be replaced with a protein identified by the discoverysystem, for example, by identifying one or more target properties of theconventional food ingredient to be replaced, and then preparing the foodproduct in which a food ingredient identified and developed according tothe discovery system as having said target properties replaces theconventional food ingredient. The disclosure also provides food productsprepared that incorporate proteins selected and evaluated by thediscovery system put forth above.

Additional aspects, embodiments, features, and characteristics of theinvention, its products, their manufacture, and use are described in thesections that follow, the accompanying drawings, and the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a discovery flywheel that can used in accordance withthis disclosure for identifying new food ingredients 800 with a targetfunction 100. The discovery system uses repeated cycles of machinelearning 700 to mine protein databases 200 for candidate proteins 300predicted to have the target function, which are then produced 400 andempirically characterized 500. Results of the testing 600 are used tonominate promising candidates for further testing as food ingredients800. The data also feeds back as part of active learning to enhancemining of the protein databases 200 and prediction of functionalproteins 300 in the next iteration of the cycle.

FIG. 2 shows several types of protein databases 201, 202, 203, and 204that may be sourced for training data and as a resource for discoveringand predicting new food ingredients that have a target function.

FIG. 3 shows how a computer system can use predictive modeling 302 ofencoded data 301 to identify and select protein candidates 303 forexperimental characterization.

FIG. 4 shows the encoding of sequence data and protein characteristicsfor training and analysis by the computer system.

FIG. 5 is a chart that shows different types of computer processes 302 ato 302 d that can be used as optional components of machine learning forpredicting protein function.

FIG. 6 shows the process flow by which candidate proteins may be sourced404 and purified 405 for empirical characterization 409.

FIG. 7 shows the subsequent steps used to characterize candidateproteins by molecular assays 501, functional assays 504, and foodscience assays 506.

FIG. 8 shows details of how assay results 601 are extracted 602 foradding to the internal protein database 204 and used to evaluate 603whether a protein candidate meet benchmarks, making it eligible fornomination as a potential food ingredient 800.

FIG. 9 shows how active learning extracts data from protein prediction300, protein production 400 and characterization assays 500 and feeds itback into the internal database 204 to increase the power of thepredictive modeling for the next iteration of the process.

FIG. 10 shows subsystem architecture of a computer system by whichprotein selection, machine learning, and data calculation may beimplemented in accordance with this disclosure.

DETAILED DESCRIPTION

The food ingredient discovery process provided in this disclosure usescomputer-driven modeling that predicts protein function from structureinformation available in protein databases. Candidate proteins areproduced and tested empirically by a high-throughput process todetermine if they have a target function and other desirable propertiesthat exceed a desired threshold or benchmark. Promising candidates arethen nominated for further development as replacement or supplementalingredients for inclusion in commercially produced food products.

Advantages of the Technology

There is considerable interest in the food industry in developing newfood sources that consume fewer resources and lessen environmentalimpact. Extensive research is under way in the use ingredients producedin plants and in cell culture. Unfortunately, plant-based productstypically lack the likeability of traditional ingredients because theydon't taste, feel, or behave like the animal or chemical products theyare replacing. If we can identify naturally occurring ingredients thatcan overcome these deficiencies or find superior products that performbetter than traditional ingredients, then environmental objectives canbe met while improving and enriching the consumer's dining experience.

The ingredient discovery and development technology put forth in thisdisclosure has several major advantages over earlier approaches:

-   -   Potential sources of natural food ingredients are not limited to        a particular catalog of plant products. Since any protein        database can be sourced by computer for initial screening,        potential sources are limited only by the extent of publicly        available knowledge of structurally characterized proteins.    -   Prediction of protein function is not limited to a simple        sequence alignment. By integrating machine learning, vector        representation of protein features, and laboratory assays, the        system learns on an ongoing basis what features are important        for a particular target function—thereby providing a wide range        of suitable candidates.    -   Using high-throughput expression and laboratory analysis as part        of the learning process anchors the search process in real-world        effectiveness. This enables the user to survey widely for        candidate proteins, and thereafter to narrow the list of        candidates for final workup. As a result, ideal food ingredients        are identified and characterized to meet particular objectives.    -   The ability to iteratively source and test proteins from a wide        range of databases is a superior approach for obtaining        ingredients from non-animal sources that mimic culinary and        sensory properties of animal sourced ingredients they are        replacing.

Iterative Prediction and Testing of a Target Protein Function

FIG. 1 is a flowchart that represents an overview of an iterative systemof procedures and events that can be implemented in accordance with thistechnology.

The user selects a target protein function 100 for a new food ingredientat the outset to guide the discovery process. Selection of the targetprotein function may be inspired by one or more hypotheses that explainin part how physicochemical properties of proteins influence proteinfunction. These hypotheses may be used to guide curation of the data.

Data processing includes curation of one or more databases 200 thatcontain relevant information on protein structure and characteristicsfor use both for computer training and as a source of new ingredients.These databases may include information from public protein and genomicdatabases, metadata obtained through partnership with otherinstitutions, and/or internal or proprietary information, such as may beobtained empirically from previous test data or predictions of proteincharacteristics and performance.

One or more protein functions are predicted 300, and candidates areselected using a combined approach of machine learning and traditionalbioinformatic analysis. The output of this process is a set of candidateproteins, which may be ranked in terms of degree of target function or acombination of desirable features. The number of proteins selected istypically limited by the capabilities of the laboratory to produce andcharacterize the candidate proteins in each cycle of the discoveryprocess.

After selection, candidate proteins are produced 400 and purified fortesting. For purposes of rapid screening of candidate proteins, theselected proteins are typically produced by recombinant expression bytransforming or transfecting a host cell line or system with apolynucleotide encoding each candidate. Proteins predicted to have thetarget function and recombinantly expressed are then characterized 500for the target function 100 and potentially for other physicochemicaland/or functional characteristics. Raw data generated by the analyticalmeasurements performed while characterizing proteins is processed toextract important features 600 to help assess performance.

Evaluation of the ability of candidate proteins to perform the targetfunction 100 may be assessed against the performance of variousingredient benchmarks or other known functional proteins within thedatabase. If a protein fails to meet the desired performance goals, itsdata is still added back into to the internal protein database toretrain the system, improving the ability to predict and mine functionalproteins 300 with the target function 100 in subsequent rounds ofdiscovery by active machine learning. If the protein does meet theperformance requirements, it may be nominated to continue development.The nominated proteins are tested as ingredients of trial food products800 to determine whether they may be used for commercial manufacturing.

Unmasking Hidden Function

The food ingredient discovery process described here uses proteins fromnatural sources in new ways. The technology put forth in this disclosurederives much of its power from its ability to discover and developproperties that were not previously appreciated for known proteins. Theowners of this technology believe there is a bounty of proteins withhidden function that can be culled as useful food ingredients, revampingthe food production and marketing business.

Some functions of naturally occurring proteins may have previously beenunknown for any of several reasons:

-   -   1. The natural source of a protein with the target function may        not be something that is traditionally considered as a source of        food ingredients;    -   2. Concentration of the protein in its natural source may be too        low for its properties to have been demonstrated in the normal        course of food product development;    -   3. The protein function may be shrouded in its natural context        by other components that have a different or more pronounced        property; or    -   4. A part of a naturally occurring protein having the target        function may be masked within the structure and function of the        rest of the protein.

The technology described in this disclosure is suited to discoverprotein function that has previously been hidden in any of these ways.In FIG. 1 , using a protein sequence database 200 as a source ofcandidates overcomes the first two of these obstacles, because itreaches beyond sources of traditional foodstuffs and brings to the foreany proteins that are predicted to have the target function, regardlessof its natural source and concentration. The third obstacle is overcomeat the production stage 400 by recombinant expression of the protein forpurposes of characterization 500. There is no need to purify a promisingcandidate from other constituents of its natural source that confoundtesting. Instead, the candidate protein needs only to be isolated fromthe host cells and other constituents of the culture broth, which is aroutine matter for most candidates produced in established cultureconditions.

Dealing with the fourth obstacle requires unmasking a promising part ofa complex protein from the rest of the protein. This is suggested wherea candidate protein scores highly in the prediction stage 300 but showsvery low target function in the characterization stage 500. The resultsof the prediction are analyzed further to identify what part of theprotein is believed to have the target function. The expression vectoris then adapted to trim the open reading frame at the 5′ and/or the 3′end of the encoded protein so that the relevant part of the protein canbe produced on its own, in the absence of other parts of the proteinthat prevent the target function from being manifest. The isolatedportion or fragment of the protein is produced and purified 400, andretested in the characterization stage 500 for the target function andother desirable properties. Protein fragmentation and extraction can bedone in this way not just to unmask or enhance the target function, butalso to eliminate other unwanted characteristics or function, or just toreduce protein bulk.

Other alterations from the structure of a naturally occurring proteinsare also permitted, if acceptable in the context of the intended use.Besides protein truncation or deletions, the protein may be adapted withone or more amino acid changes to create a variant of the naturallyoccurring protein or fragment thereof, thereby adding a desiredproperty, removing an undesired property, or for any other reason. Suchvariants are typically at least 95%, 98%, or 99% identical in terms ofamino acid sequence relative to the naturally occurring protein or afragment thereof.

Alternatively or in addition, the user may use recombinant technology tobuild a protein candidate, fragment, or variant thereof having thetarget function into a larger fusion protein or protein assembly. Thefragment having the target function is conjoined or coexpressed with oneor more other proteins or fragments during recombinant expression. Theother components of the fusion protein or protein assembly may beselected from proteins known to have other beneficial properties, ordiscovered by using the technology described here in search of the sameor a different target function.

Exemplary Target Functions

The technology of this invention can be used for the purpose ofidentifying replacement ingredients that are more desirable in foodproducts for one reason or another, replacing an ingredient that istraditionally used in a food recipe or formula, but for one reason oranother should be replaced. Ingredients may be more desirable—forexample, because they are obtainable from a more sustainable orenvironmentally friendly form of architecture or harvesting, becausethey are less expensive to produce, or because they have otherbeneficial characteristics. Once an ingredient in a foodstuff isselected for replacement, the user identifies a target protein function100, which becomes the object that guides the iterative process shown inFIG. 1 .

Exemplary target functions include the following: gel-formingproperties; foaming agents; carriers for flavor, color, vitamins,porphyrin, heme, or carbohydrate; moisture retention; antimicrobialactivity and other preservation functions; fat structuring (for example,for oleogel creation); adhesive and film forming agents; ingredientswith enzymatic or hormonal function; emulsifying agents; nutritionalsupplementation (such as casein); viscosity alteration or moistureretention; agents that cause flocculation or adhesion; fiber; andstructural components that support scaffolds.

By way of example, the ingredient discovery system put forth in thisdisclosure can be focused on gelation as a target function. Theobjective would be to identify a high strength gelling agent, similar toegg white protein, that is non-allergenic, designed to bind ingredientsat low concentrations, and suitable for cooking. Egg is frequently usedas a binding or gelling agent to hold other ingredients together infoods like processed meat products, baked goods, and confectionary. Eggcomponents are also used in many alternatives to processed meat,including vegan equivalents of sausages and meat patties. Currently, eggingredients are relatively inexpensive, whereas plant proteins thatpromote gelation are in relatively low abundance in agriculturalproducts, making them difficult and expensive to use as substitutes. Amore easily sourced protein having suitable gelation properties isdesirable to replace egg in many food products. Finding a naturallyoccurring gelation substitute that can be easily purified or producedrecombinantly would transform the way many of these foods is made.

Source Databases

The information databases 200 used as a potential source of data forproteins having the target function generally come in two forms: publicdatabases, including information such as protein amino acid sequence andcrystal structure, and possibly other protein characteristics such asphysicochemical properties and natural sources. There may also be aninternal database that collects information not only on proteinstructure, but also physicochemical and functional characteristics thatare tested or assessed as part of the protein discovery process.

FIG. 2 shows an arrangement of databases that may be used as informationsources for the protein discovery process. Protein sequence databases201 typically contain information related to the amino acid sequence ofthe protein, including alternative isoforms and sequence variants. Thesequence databases may also contain functional annotations about theprotein, including its primary function, source organism, cellularcomponent, and metabolic pathways. Exemplary protein databases areUniProt/SwissProt, UniProt/Trembl, PFAM (a database of curated proteinfamilies, each of which is defined by multiple sequence alignments and aprofile hidden Markov model), ProteinNet, Uniparc, and Uniref90.

Protein structure databases 202 typically contain information on thethree-dimensional configuration of proteins that define their secondary,tertiary and quaternary structure, gathered from such techniques asX-ray diffraction, nuclear magnetic resonance, and cryo-electronmicroscopy. Detailed information may include atomic-level coordinatesand amino acid level assemblies. Local structure data may includefeatures such as alpha helices and beta sheets. Exemplary structuraldatabases include the Protein Data Bank (PDB), the StructuralClassification of Proteins database (SCOP), the Pfam database, and theCATH Protein Structure Classification database.

Genomic sequence databases 203 contain nucleic acid sequence informationorganized at the organism, chromosome, gene, and transcript level.Besides the encoded protein, genomic sequence databases containinformation that is upstream or downstream from the reading frame, andin introns. Genomic sequence data can be used computationally to infermultiple open reading frames or multiple isoforms of the same protein.Exemplary genomic or nucleic acid sequence databases include MIPhytozome, NCBI Refseq, NCBI Genome, and the Plant Genome Database(PGDB).

The internal protein database 204 may contain structural data forproteins, and information generated experimentally from proteinselection, expression, purification, and characterization.

In the context of machine learning and data mining in accordance withthis disclosure, general reference to a protein database or aninformational database may refer to any one of these databases or aselection thereof in any combination.

Predicting Protein Function

Protein information sourced from the databases is analyzed by computerto predict whether each protein in the databases or a selection thereofhave the target function.

FIG. 3 shows steps typically used in the process of predicting andidentifying functional proteins 300. The computer system performs dataencoding 301 and predictive modeling 302. This produces a list ofcandidate proteins 303 for experimental characterization.

The data is encoded 301 in vector or matrix form to be processed by themachine learning models. Continuous features can be normalized and/ordiscretized. Categorical features are one-hot encoded, binary encoded,or hash-encoded. Protein amino acid sequences can be transformed so thatthe dimensionality of the space they are lying in is reduced. Sequencesand additional features for protein of various lengths are encoded in afixed sized matrix. This is done with word-bagging, with autoencoders orwith encoder-decoder models such as Seq2seq (Sutskever et al.,arXiv:1409.3215, 2014) or Transformers (Vaswani, et al.,arXiv:1706.03762, 2017). Models that generate embeddings (a fixed sizevector representing a sequence or a single residue) are trained on largeamounts of unlabeled data.

Input data for predictive modeling may include one, two, three, or morethan three of the following features for each protein, sourced from oneor more databases:

-   -   amino acid sequence;    -   three dimensional structure, obtained from crystallography data,        predicted algorithmically from a protein's amino acid sequence        (for example, using AlphaFold 2.0™, AW Senior et al., 2020,        Nature 577 706-710), or obtained from a three-dimensional        database (such as the AlphaFold™ Protein Structure Database from        Google's DeepMind and EMBL-EBI);    -   residue-level features, encoded as a set of vector        representations for physicochemical and structural features of        each amino acid and/or clusters of two or more amino acids that        are proximate to each other in sequence or in three-dimensional        space, typically predicted from amino acid sequence;    -   protein level features encoded for the protein as a whole (such        as amino acid length, overall charge, hydrophobicity, presence        of structural features such as alpha helices and beta-pleated        sheets, and protein crosslinks), predicted from amino acid        sequence, three dimensional structure, or determined        empirically; and    -   results from empirical assays done as part of the        high-throughput expression and screening during the discovery        process.

Residue level features can be sourced using AAindex, a database ofnumerical indices representing various physicochemical and biochemicalproperties of amino acids and pairs of amino acids. There are threesections: AAindex1 for the amino acid index of 20 numerical values,AAindex2 for the amino acid mutation matrix and AAindex3 for thestatistical protein contact potentials. All data are derived frompublished literature. S. Kawashima et al., Nucleic Acids Res 2008;36:D202-5.

Input data in each category can be categorical, or continuous.Categorical data is defined as variables that contain labels instead ofnumerical values. Examples of protein categorical data are proteinfamily, cellular location, and source organism. Depending on the natureof a target function or a protein characteristic, the feature may becoded as a categorical variable or a continuous variable. Categoricaldata are defined as variables that contain labels instead of numericalvalues. Examples of protein categorical data are protein family,cellular location, and source organism. Continuous or numerical data arevalues that are composed of numbers. Examples of protein continuous dataare molecular weight, isoelectric point, and percentage of each aminoacid type.

FIG. 4 shows a suitable data encoding process. Sequences, residue levelfeatures, and protein level features are merged and encoded. The encoderlearns how to represent features of a protein in a compressed space in away that it can be reconstructed and compared with data from otherproteins. Additional protein features for each protein are normalizedand discretized, and merged into the encoded data.

In situations where only a few data points are labeled out of a largerensemble, a process of active learning or retraining may be used todrive the labeling of new data. Iteratively, given a predefined querystrategy and model behavior on labeled data, new data points are pickedfor labeling and the model parameters are updated. In practice, thismeans augmenting the current dataset with new proteins that are lesslikely to perform well given the current model (for example,representing groups with higher misclassification or higheruncertainty).

The training or test data set is constructed as follows. Proteinssequences contain regions of variable conservation due to selectivepressures on random amino acid changes. Therefore, their sequence is notindependent and identically distributed (IID). Since IID is arequirement for train-test splitting and cross-validation (CV), proteinsare clustered according to their sequence or MSA similarity first. Thenthe clusters are shuffled, and a split is performed among the clusters.

FIG. 5 shows various types of machine learning that can be brought tobear for predictive modeling 302.

Machine learning (ML) 302 a is a method of data analysis done bycomputer that automates analytical model building. It is a branch ofartificial intelligence based on the idea that systems can learn fromdata, identify patterns and make decisions with minimal humanintervention. T. Mitchell, Machine Learning. New York: McGraw Hill,1997.

The paradigm of machine learning 302 a incorporates two phases: thetraining phase and the inference phase. During the training phase,protein sequences, residue level features, protein level features areprovided to the model as input. Additionally, protein targets areprovided to the pre-defined loss of model. The loss function calculatesthe loss used by the optimizer to update the model parametersiteratively until convergence. The result of this operation is a set offixed parameters that are used at inference time. The sequences andfeatures at residue and protein levels are generated the same way atinference time as during training.

For protein features that are categorical, the prediction task isclassification, classification losses (e.g., cross entropy) and metrics(e.g., AUROC). For example, if the target function is gelation, a binarycategory may be used depending on whether a particular protein gels ornot. For protein features that are continuous (such as degree or scopeof antimicrobial activity), the prediction task is calculation ofregression losses (e.g., MSE) and metrics (e.g., r²). Using the exampleof the gelation property, the function can be defined using a valuex={0, 1}, where x=0 represents the absence of any gelling while x=1represents the highest measured gelling value observed. The regressiontask is to predict the continuous value of x for a new protein.

Deep learning (DL) 302.b may also be used for predictive modeling. Deeplearning is a class of machine learning algorithms that uses multiplelayers to progressively extract higher-level features from the rawinput. Each level learns to transform its input data into a slightlymore abstract and composite representation. Bengio et al., IEEETransactions 35: 1798-1828, 2013; Deng et al., Foundations and Trends inSignal Processing. 7: 1-199, 2014; Lecun et al., Nature. 521: 436-444,2015. DL is a sub-ensemble of the machine learning techniques, usingdifferent architectures, more model parameters, and allowing forunstructured input data. It relies on the successive application ofdifferentiable transformations on the input data. The sequence oftransformations defines the architecture of the DL model (for example,convolutions, pooling, and rectifier are the transformations that defineConvolutional Neural Networks (CNN)).

Homology modeling 302.c leverages bioinformatics tools that can comparegenes, transcripts, and proteins to identify similar entities which mayshare common functional characteristics. Proteins that share similarsequence, structure, and family annotations can be inferred to servesimilar functions in the context of food ingredients. One such exampleis the BLAST (basic local alignment search tool) software providedthrough the National Center of Biotechnology Information that can findregions of nucleic acid or amino acid homology between a target sequenceand databases of query sequences. Since homology modeling methods do notrequire experimental data generated in the internal protein database,these analytical tools can be applied before proteins are produced forempirical testing.

Combinations of these and other forms of machine learning may bereferred to in this disclosure as hybrid or multimodal machine learning.Baltrušaitis et al., arXiv:1705.09406v2, 2017.

The ensembling process 302.d takes as input the predictions of the othermodels (302.a, 302.b, 302.c). In practice, ensembling performs aweighted average of predictions of protein function that are made indifferent ways. The set of weights (for the average) is optimized tominimize a predefined loss function on a set of unseen data points.Those weights can be arbitrarily defined to give more or less predictionpower to each of the models used based on an expert's input.

The output of the predictive modeling 302 is a list of proteins 303 thatis potentially ranked or sorted by relevance to the target proteinfunction, optionally influenced by other desired features. The chosenproteins or a subset thereof is subsequently characterized by aplurality of criteria tested in different assays. Each criterion may beconsidered to have high, neutral, and no relevance to the target proteinfunction. The high relevance criteria likely yield functional proteinssuitable for further workup. The neutral and no relevance criteriagenerate data that can be used for the purpose of refining thepredictive models in further cycles of active learning. The machinelearning may be set to group similar proteins together; and/or topredict protein function from structure and other characteristics.

Protein Production

FIG. 6 is a flow chart that outlines a process by which proteinsselected from the list generated in silico 303 may be produced forempirical testing. A decision is made 401 as to the source and mode ofproduction: either from natural sources, by recombinant expression, orby chemical synthesis. If proteins are obtained from a native source,they pass directly to the purification step 405 while recombinantproteins are made in the expression stage 402. If the sequence of aprotein or peptide is short and does not require modifications, theprotein may be produced by solid-phase synthesis, whereupon they passdirectly to characterization 409.

Amongst these choices, recombinant protein production is typically usedfor high throughput screening, allowing a list of proteins to beassessed at the same time in the same way. Recombinant production isdone by genetic modification of an expression host 402. Cell lines(cultures of animal cells), microorganisms (yeast, fungus, or bacteria),plants (such as algae or wheat), or cell-free extracts (for example,that contain material extracted from expression-competent cells) mayserve as a host. The host is genetically modified (through infection,transformation, or transfection) to integrate DNA or carry plasmidsdesigned to express the protein of interest constitutively or viainduction. Genetic modification may also include the use of sequencesthat modify the protein by adding DNA that encodes for peptide or smallauxiliary protein tags. The tag can be used for downstream purificationand characterization. Reference books on the subject include RecombinantGene Expression, A. Lorence ed., 2012; New Bioprocessing Strategies, B.Kiss et al. eds., 2018; and Cell-Free Synthetic Biology, S. Hong ed.,2020.

Suitable organisms used for recombinant expression of candidate proteinsare listed in Table 1. Host organism selection is done taking intoconsideration the ability for the host to express soluble protein inhigh quantities with the post-translational modifications (such asaddition of carbohydrates and/or interchain crosslinking) that mayaffect protein function.

TABLE 1 Recombinant expression systems for candidate proteins OrganismStrain animal Drosophila S2 animal SF9 animal SF21 animal CHO yeastPichia pastoris (Komagataella phaffi) yeast Saccharomyces cerevisiaefilamentous fungi Aspergilllus filamentous fungi Trichoderma reesifilamentous fungi Neurospora crassa bacteria E. coli plant Nicotianabenthamiana plant Solanum lycopersicum algae Chlamydomonas reinhardtiicell free plant extract cell free bacteria extract cell free yeastextract

Eukaryotic expression systems have the advantage of performingpost-translational processing of protein candidates in a manner akin towhat may be used naturally or for industrial production, such asglycosylation and interchain crosslinking Prokaryotic expression systemshave the advantage of being easy to implement and obtain high yield. Itis possible to use several systems during development: for example,expression in E. Coli for performing screening assays; and expression ineukaryotes for later stage development and testing. Some expressionsystems such as yeast are suitable for use in both stages.

The expression product is evaluated 403 for solubility of the proteinand yield. Proteins are preferably water or buffer soluble and expressedat high enough yields to be used for downstream characterization.Solubility and expression data on a specific protein may be used toevaluate the potential for a protein to be generated in largerquantities. Techniques such as gel electrophoresis, capillaryelectrophoresis, and ELISA can be used to determine the presence of atagged protein, check molecular weight of the protein, and provide yieldevaluation. Protein solubility can be tested by fractionation usingfiltration, gravity, or centrifugation followed by analysis of thesoluble aqueous phase to determine if the protein presence. The amountof soluble protein required from this step is dependent on therequirements for the biochemical and materials characterization, wherespecific assays selected depends on the target function of interest. Ifproteins achieve the solubility and yield criteria, they are thenpurified. If expression of a protein does not pass, the data iscollected in the internal protein database for purposes of predictingother protein candidates and expression potential. Alternativeexpression systems may also be tested with a view to increasing yield ifa candidate protein is considered promising for other reasons.

Materials for recombinant purification are sourced 404 from fermentationof host organisms using standard fermentative procedures such as plate,flask, or bioreactor fermentation. Natural source materials can beobtained from whole or isolated fractions from fungi or plants.

Protein purification 405 is optional if characterization assays do notrequire pure protein. For example, enzymatic activity of a protein maybe assessed using a mixture of proteins and may not requirepurification. The purification strategy will vary depending on thesource (native or recombinant) and the level of purity needed forcharacterization assays. Both recombinant proteins and native sourceproteins may be purified using standard purification procedures. Bothrecombinant and native sourced proteins can use methods for proteinisolation including dry and wet processing.

Common purification methods include centrifugation, filtration, affinitychromatography, ion exchange chromatography, size exclusionchromatography, hydrophobic interaction chromatography, affinitycapture, isoelectric precipitation, liquid-liquid phase separation(LLPS), lyophilization, and dialysis. One of these methods may be usedas a single step or combined with other methods as needed to achieve adesired level of purity. Once achieved, the protein is processed bystandard methods into a final condition that is compatible withcharacterization methods. For example, some assay methods may requirepowdered protein, while other characterization methods may requireproteins in aqueous solution. Reference books on this topic includeProtein Purification, 2nd Ed., P. Bonner, 2018; and High-ThroughputProtein Production and Purification, R. Vincentelli ed., 2019.

To facilitate protein purification (particularly for high-throughputempirical testing of protein candidates), recombination protein can beexpressed with an exclusive tag for affinity binding. In this context, a“tag” is any feature added to the protein during expression that can beused as a handle for affinity purification using a conjugate bindingpartner. Examples include amino acid sequences added internally or toeither end of the naturally occurring protein sequence, andcarbohydrates. By way of illustration, an additional sequence of aminoacids (perhaps at least 5, or between 5 and 50, or 8 and 25 amino acidsin length) can be included in the open reading frame (typically at theN- or C-terminus) that is recognized by a binding partner such as aconjugate receptor, antibody, or other binding protein. Another exampleis an embedded protein sequence that acts as a recognition site forcarbohydrate-loading enzymes, creating a glycosylation feature that canbe captured with a conjugate binding moiety such as a lectin.

Suitable protein tags include poly-histidine that binds to metals suchas nickel, cobalt, or zinc, GST protein that binds to glutathione, andc-myc protein that binds to anti c-myc antibodies. Other alternativesarea flag tag (the 8-amino acid sequence DYKD followed by DDDK) which iscaptured using anti-flag antibodies, or the CL7 tag, available fromTriAltus Biosciences, which binds to an IM7 resin. After the taggedprotein is immobilized on an affinity surface, fermentation byproductscan be washed away. Depending on the tag used, the purified targetprotein can then be eluted from the resin using competitive binding or acondition change, such as pH.

For purposes of initial screening, the tag can be left on the proteinafter purification, unless there is a concern that it might interferewith the functional assays. For later-state testing or preparing afinished product, the open reading frame may include a specificproteolytic cleavage site between the tag and the rest of the protein. Acleavage enzyme, such as tobacco etch virus (TEV) protease, can beincubated with the protein to remove the tag. The cleaved tag, anyuncleaved recombinant protein, and the cleavage enzyme can then beremoved by other means, leaving the purified target protein.

The next step 406 is to assess whether chemical modification isrequired. Purified protein samples may undergo chemical modification forcertain target functionalities of interest. Modifications may includehydrolysis to produce protein fragments, crosslinking of proteins, orother enzymatic treatments. Chemical or enzymatic modification resultsin a modified protein sample 407, which is then evaluated for targetmetrics similarly to proteins that did not undergo modification.

Target formulation 408 of a protein preparation typically is a stableformulation that is compatible with the characterization methods. Forexample, characterization by a specific biochemical characterizationmethod may require a solution state protein with targeted solutionidentity, while other characterization methods may rely on protein to bein dried form. Protein state, purity, concentration, solubility, andother features of the preparation may be assessed at this point. Gatingmetrics are typically protein purity, protein concentration, and (to theextent required) protein solubility. If the target formulation 408 isachieved, the protein sample is ready for characterization 409.

Protein Characterization

Protein preparations that are produced, purified, and modified as neededmay then pass to the characterization phase 500. Proteincharacterization typically includes molecular, functional, and foodscience assays. Initially, all proteins may be evaluated in these assaysto survey the candidate proteins to gain a range of output values. Eachtime through the discovery cycle, the number of characterized proteinsincreases, and it may be appropriate to reset the thresholds so thatonly highly promising proteins advance to the next step ofcharacterization. Individual steps in this section generate data andmetadata that is specific for each assay type for storing in theinternal protein database.

FIG. 7 illustrates the characterization phase. Molecular assays 501 thattest physicochemical properties are used provide detailed biochemicaland structural information for a protein of interest. Useful propertiesto test at this stage are illustrated in Table 2.

TABLE 2 Assessing biochemical properties Biochemical property Assaysoligomerization state size exclusion chromatography, native pageconcentration Bradford ™, Pierce 660 ™, absorbance spectroscopy purityamino acid analysis, proximate analysis, gel electrophoresis, capillaryelectrophoresis buffering capacity titration pH indicator strips, pHprobe enzyme activity colorimetric assays, fluorometric assays,absorbance spectroscopy molecular weight gel electrophoresis, capillaryelectrophoresis degradation gel electrophoresis, amino acid analysisconductivity conductivity probe % random coil circular dichroism % alphahelix circular dichroism % beta sheet circular dichroism zeta potentialphase analysis light scattering solubility fluorometric assays,colorimetric assays aggregation dynamic light scattering,centrifugation, size exclusion chromatography, fluorescence-based assaysparticle size distribution dynamic light scattering melting temperature(t_(m)) differential scanning calorimetry, thermal shift assay heatcapacity differential scanning calorimetry, thermal shift assay surfacehydrophobicity fluorometric assay % thiols fluorometric assay sulfurcontent fluorometric assay density biophysical glycosylation contentmass-spectroscopy

Data from the molecular assays 501 are usually stored in the internaldatabase for use in retraining the predictive model, regardless of theresult. Minimum criteria can be set to decide 502 which samples pass tofunctional assays 504. In the first rounds of the protein discovery, theuser may decide to let all proteins pass through to functional assays,with the objective of building up the set of data used for training inthe internal database 204. When predictive power of the models increasesfor a particular target function, the minimum criteria may be increased502 to select only the most promising proteins to move to functionalassays. Performance of the expressed proteins may also be compared withthe performance of commercially available ingredient benchmarks 503,which are evaluated in functional assays 504 and in some cases foodscience assays 506. The benchmark ingredients may include animal-sourcedingredients as well as plant-based or synthetic ingredients that containprotein, starch, or lipid components.

Functional assays 504 performed on protein candidates include testingfor the target function. Additional assays are typically included tocharacterize candidate proteins in other ways: such as for the presenceof other desirable properties, the absence of undesirable properties,and other functions that may be collateral with the target function, andtherefore relevant for the predictive modeling. Examples of suchfunctional assays are listed in Table 3.

TABLE 3 Assessing functional properties Functional property Assaysgelation rheology aggregation dynamic light scattering texture textureprofile analysis particle size dynamic light scattering viscosityviscometry sol gel transition temperature rheology denaturationtemperature differential scanning calorimetry heat capacity differentialscanning calorimetry chewiness texture profile analysis colorcolorimeter storage modulus rheology shear strength rheology densitydensitometry swell ratio w/water mass measurement sedimentation layerthickness emulsion stability analysis via multiple light scatteringsedimentation migration rate emulsion stability analysis via multiplelight scattering emulsion stability index, emulsion stability analysisvia coalescing phase multiple light scattering coalescence time emulsionstability analysis via multiple light scattering coalescing layerthickness emulsion stability analysis via multiple light scatteringcoalescence migration rate emulsion stability analysis via multiplelight scattering emulsion stability index, emulsion stability analysisvia flocculating phase multiple light scattering time to flocculationemulsion stability analysis via multiple light scattering flocculationlayer thickness emulsion stability analysis via multiple lightscattering flocculation migration rate emulsion stability analysis viamultiple light scattering max foam volume foam analysis via imaging maxliquid volume foam analysis via imaging gas volume foam foam analysisvia imaging foam capacity foam analysis via imaging maximum foam densityfoam analysis via imaging foam expansion rate foam analysis via imagingfoam half life time foam analysis via imaging drainage half life timefoam analysis via imaging temperature at gelation point rheology yieldstress rheology cohesiveness texture profile analysis adhesivenesstexture profile analysis gumminess texture profile analysis meltingpoint differential scanning calorimetry water binding capacity moistureanalysis critical micelle concentration dynamic light scatteringcritical concentration for gelation rheology critical concentration formoisture analysis water binding antimicrobial action microbial growthassays, fluorescent dye permeabilization, NMR spectroscopy

The assays used in the characterization process may be standard ordeveloped in-house. The project may include adapting assays tohigh-throughput formats or adapting typical food assays to probe aspecific function of interest.

The properties of the target protein are measured and compared withbenchmark samples selected to demonstrate the performance of the targetprotein with respect to commercially available ingredients. On thisbasis, a decision is made 505 as to which protein candidates proceed tofood science assays 506. Promising candidates are tested in food modelsystems to validate the target protein's performance in a simplifiedfood formulation. The performance information is stored in the internalprotein database 204 and used to assess which proteins should bedeveloped into products.

FIG. 8 provides a more detailed illustration of extracting features andanalyzing the data 600. The raw data generated by characterizationassays can vary widely by the assay type. Some common examples of dataoutputs include endpoint data, scalar values, sequences/series of scalarvalues (for example, time or temperature sequences), or images. The rawdata are analyzed to extract meaningful trends.

Depending on the assay type, assay results for the protein candidates601 can be tabular flat files, image files, or numerical values. Thenumerical values are interpreted as is. Tabular flat files and imagefiles are processed to extract data features 602. The output may be acomplete set of empirical data for the proteins that were characterized,which is used to evaluate whether the protein performed well and isentered into the protein database. The extraction process can comprisecomputing aggregated numerical values (such as mean or median of timeseries data) or extracting categorical values (such as color ortransparency from images).

Each target protein function 100 is associated with a specific set offunction specific properties 604 that can be used to determine whether aprotein candidate is nominated as a potential food ingredient 800. Thefunction specific properties 604 is a subset of biochemical andfunctional properties such as those listed in Table 2 and Table 3 thatare related to target protein function and use of the candidate proteinas a food ingredient. For example, if the target protein function 100 isfoaming, then properties measured by the solubility, surfacehydrophobicity, and foam analysis via imaging assays may be relevant forevaluation of the candidate proteins. Function specific properties 604of a candidate protein are compared with benchmark thresholds 603 thatare pre-established or developed during the course of discovery. Thecompared values are used to determine whether each protein candidate hassufficient target function 100 and other desirable properties at a levelor combination that make it worthy to be nominated as a functionalprotein ingredient 800.

Active Learning

FIG. 9 illustrates how technology in this disclosure may incorporateiterative active learning or retraining as part of the protein screeningand characterization process. Information from the prediction andselection of protein candidates 300, protein production and purification400, and the characterization of biochemical ad functional properties500 provides useful data that can be extracted 602 and added to theinternal protein database 204 for use in further training of thecomputer system.

If n is the number of iterative predictions run for a particular targetfunction, then at n={0,1}, the internal protein database 204 will beempty. The ensemble methods will only be able to leverage protein datafrom the protein sequence, protein structure, and genomic sequencedatabases. For all n>1, additional information is available aboutselected and tested candidate proteins for the target function, which isadded back into the internal protein database 294. The data for anyiteration of n>1 will be used in the predictive modeling for iterationn+1. As the internal protein database will contain iteratively moreinformation in n+1 than n, the predictive accuracy at n+1 will usuallybe higher than n.

Species Homologs and Isoforms

Proteins that play an important functional role in a botanical,zoological, or microbial context generally have homologs in closelyrelated species of the source. A protein may also evolve within aspecies by gene duplication to create different isoforms. If a proteinin a database scores high in the computer-driven predictive phase ofthis technology, there is an increased probability that species homologsand isoforms will also score high in the predictive phase.

It therefore can be beneficial to screen out homologs and isoformsduring initial iterations of the discovery process so as to survey abroader range of unrelated structures. One homolog or isoform isselected for testing that represents the class. This can be done bytemporarily removing homologs and isoforms from the list of candidatesgenerated by the machine learning process, either by operatorsupervision or incorporation into the computer programming. Once aparticular candidate is characterized empirically as having a high levelof target function and other benefits, it may be appropriate to go backto the homologs and isoforms identified by the computer in the sameclass, producing and characterizing them separately so that the user canoptimize the protein ultimately chosen as the food ingredient.

Screening for Additional Functional and Physicochemical Properties

The iterative discovery process of this disclosure optimally includesassessing whether the protein candidate has one or more additionaldesirable functions or properties, thereby increasing the favorabilityrating of the candidate—and assessing whether the protein candidate hasone or more undesirable functions or properties, thereby decreasing thefavorability rating of the candidate or removing it from contention. Byway of illustration, desirable properties may include one or more of thefollowing: ease of expression, ease of purification, stability onstorage, mixability, and one or more desirable flavors or sensoryproperties. Undesirable properties may include one or more of thefollowing: allergenicity or immunogenicity, incompatibility with otherfood ingredients, an adverse physiological effect, and an undesirableflavor.

Where computer prediction algorithms are available for such properties,the assessment may be done as part of the initial candidate selectionprocess during protein screening and selection. The prediction algorithmfor the respective property is used as part of scoring for eachcandidate, and optionally contributes to the machine learning function.For some categories such as toxicity, taste, and mouthfeel, assessmentis done in the assay and empirical testing phases, or a combination ofthese with machine learning.

For example, allergenicity can be predicted in the manner of L. Zhang etal., Bioinformatics 2012, 28:2178-2179; L. Wang et al., Foods 2021,10:809, doi.org/10.3390; and S. Saha et al., Nucl. Acids Res. 2006, 34,doi:10.1093. Immunogenicity can be predicted in terms of MHG bindingmotifs and T and B cell epitopes algorithmically in the manner of N.Doneva et al., Symmetry 2021:13, 388. Toxicity can be predicted in themanner of S. S. Negi et al., Sci. Reports 2017:7, 13957-1; and Y. Jin etal., Food Chem. Toxicol. 2017; 109:81-89. Aspects of flavor can bepredicted in the manner of P. Keska et al., J. Sensory Studies2017:e12301; F. Fritz et al., Nucleic Acids Res. 2021 Jul. 2; 49(W1):W679-W684′ and S. Ployon et al., Food Chem. 2018 Jul. 1; 253:79-87.

Further Development and Approval of Functional Proteins as FoodIngredients

By putting this technology in place, the user can obtain a catalog ofwell categorized, functional protein ingredients with food-relevantfunctionalities. New ingredients identified by this technology may beproduced for incorporation into commercial products by recombinantexpression, either in the same form they occur in nature, or byproducing only the parts of the protein that provide the targetfunction. Knowledge of the ingredient source, method of scalableproduction, and a full panel of biochemical and functionalcharacteristics that is generated as part of this discovery process isinformation that can be used to commercialize the newly discoveredingredients in a wide range of important applications.

After a new food ingredient has been identified according to thisdisclosure and formulated into a proposed new product, the developerwill assure that all regulatory requirements are met before beginningcommercial distribution. New food additives for distribution in the U.S.are subject to premarket approval by the Food and Drug Administration(FDA). The new additives are “generally recognized as safe” (GRAS) ifthere is generally available and accepted scientific data, information,or methods indicating it is safe, optionally corroborated by unpublishedscientific data. A notification sent to FDA's Office of Food AdditiveSafety for approval includes a succinct description of the substance(chemical, toxicological and microbiological characterization), theapplicable conditions of use, and the basis for the GRAS determination.The FDA then evaluates whether the submitted notice provides asufficient basis for a GRAS determination.

Other Implementations of the Discovery Process

Some implementations of the flywheel or discovery process put forth inthis disclosure are a combination of the following methodologies:

-   -   Machine learning of how structural features of proteins (such as        primary amino acid sequence, three-dimensional structure, vector        representations, and known physicochemical properties) can be        used to predict whether a previously uncharacterized protein has        a target function;    -   Computer based mining of extensive sequence, structure, and        functional databases to select protein candidates predicted to        have the target function;    -   High-throughput expression and empirical testing of the        candidates for the target function and other desirable        characteristics;    -   Reiteration of the learning, database searching, expression, and        testing to refine the selection process and select additional        candidates.

In the preceding discussion, the discovery process has been illustratedby the selection and evaluation of potential new food ingredients tosubstitute for ingredients currently in widespread use and/or obtainedfrom animal sources. The discovery process is equally suitable foridentifying proteins that can substitute for or enhance functions inother industrial products and materials. Other possible applications ofthe discovery process include identifying proteins having the followingpotential uses in commerce:

-   -   ingredients for cosmetics    -   structures for moisture retention    -   binders for dyes    -   optimized fermentation for manufacture of biofuels    -   starting materials for polymer chemistry and plastics    -   lubricants, surfactants, solubilizers, and dispersion enhancers    -   coatings, ceramics, ink, and textiles    -   agricultural feed having increased nutritional value    -   encapsulation means, excipients and stabilizers for products in        pharmaceutical industries.

Such alternative implementations of the discovery process representalternative and included embodiments of the invention put forth in thisdisclosure. They may be claimed as additional or alternative aspects ofthis disclosure by adapting the claims presented below mutatis mutandisgenerically or in accordance with the selected or desiredimplementations.

Computer Hardware and Software

As a general matter, computer systems or microprocessors referred to inthis disclosure are designed, manufactured, controlled, and programmedin accordance with standard methodology.

FIG. 10 shows an arrangement for a computer system that is either asingle apparatus or assembly, or an interconnected plurality thereof.Subsystems of the computer system are typically interconnected via asystem bus 1012. Subsystems may include a printer 1004, keyboard 1008,fixed disk 1009, and monitor 1006, which may be operably connected to adisplay adapter 1005. Peripherals and input/output devices coupled to anI/O controller 1001 may be operably connected to the computer system bya suitable means such as a USB port 1007 and/or an external interface1011, which may also connect the computer system to wide area networksuch as the Internet. Interconnection of subsystems via the system bus1012 allows the central processor or microprocessor 1003 to communicatewith each subsystem and control the execution of instructions fromsystem memory 1002 or other memory means such as a fixed disk 1009, aswell as the exchange of information between subsystems.

External databases containing useful information, such as information onprotein sequence, structure, and characteristics, may be sourced througha public network such as the Internet. Internal databases of informationmay be part of the computer system or sourced through a secure network.When information is sourced in the course of calculating, evaluating, ormachine learning in accordance with this disclosure, the information maycome from one or a combination of different databases that are externaland/or internal. The computer system may transfer information orcalculations from one component to another component or outputinformation to a user, who can input information or direction back intothe computer system and thereby to its components.

Operations or functions referred to in this disclosure may beimplemented as software code to be executed by a processor. Machinelearning languages include Python, Pytorch, Scala, Java, R Programming,Javascript, Lisp, SageMaker, and C++. Reference books on the subjectinclude Data-Driven Science and Engineering, S. L. Brunton, 2019;Machine Learning for [patent attorneys and other] Dummies, J. P.Meuller, 2^(nd) Ed, 2021; and Deep Learning, I. Goodfellow et al., 2016.

The software code may be stored as a series of instructions or commandson a computer readable medium for storage and/or transmission, such asrandom access memory (RAM), a read only memory (ROM), a magnetic mediumsuch as a hard-drive, an optical medium such as a DVD (digital versatiledisk), flash memory, or in information packets downloadable from avendor or source via an electronic network. Any of the methods referredto in this disclosure may be totally or partially performed with acomputer system configured or programmed to perform the steps of themethod, in combination with or independent from input or supervisionfrom a user. Method steps referred to in this disclosure that areperformed entirely or in part by a computer system are optional unlessotherwise stated or required.

Incorporation by Reference

Each and every publication and patent document cited in this disclosureis hereby incorporated herein by reference in its entirety for allpurposes to the same extent as if each such publication or document wasspecifically and individually indicated to be incorporated herein byreference.

Interpretation and Implementation

Although the technology described above is illustrated in part bycertain concepts, procedures, and information, the claimed invention isnot limited thereby except with respect to the features that areexplicitly referred to or otherwise required. Theories that are putforth in this disclosure with respect to the underlying mode ofproduction, action, and assessment of various products and componentsare provided for the interest and possible edification of the reader,and do not limit practice of the claimed invention. The reader may usethe technology put forth in this disclosure for any suitable purpose.

While the invention has been described with reference to the specificexamples and illustrations, changes can be made and substituted to adaptto a particular context or intended use as a matter of routinedevelopment and optimization and within the purview of one of ordinaryskill in the art, thereby achieving benefits of the invention withoutdeparting from the scope of what is claimed below and equivalentsthereof

The invention claimed is:
 1. A method of identifying and developingindividual proteins as food ingredients, the method comprising: (a)training a computer system to predict whether an individual protein hasa preselected target food function from structural characteristics andproperties of the protein, wherein the target food function is a desiredeffect of the individual protein on a commercial food product whenincorporated into the food product as an ingredient, wherein thestructural characteristics and properties of the protein used to trainthe computer system include at least the protein's amino acid sequence,wherein the computer system is trained by a process of machine learningthat comprises inputting into the computer system a training data setthat contains said characteristics and properties for a plurality ofindividual proteins known to have the target food function and for aplurality of individual proteins known not to have the target foodfunction; (b) applying the computer system trained in step (a) to adatabase that contains said characteristics and properties for each of aplurality of individual proteins for which it is not previously knownwhether the individual proteins have the target food function, therebypredicting which of the individual proteins in the database have thetarget food function; (c) identifying or ranking by the computer systemthe individual proteins predicted in step (b) to have the target foodfunction, thereby obtaining a set of protein candidates; (d)recombinantly expressing and purifying each of the protein candidates;(e) conducting assays to determine or quantify which of the expressedprotein candidates have the target food function; (f) adding structuraldata and/or assay results for the protein candidates tested in step (e)into the training data set; (g) selecting one or more of the individualproteins assayed in step (e) as potential food ingredients if they aredetermined in step (e) as having the target food function above a chosenthreshold; (h) performing additional cycle(s) of steps (a) to (g) untila desired number of individual proteins have been selected as potentialfood ingredients having the target food function above the threshold;and then (i) assessing one or more individual proteins selected aspotential food ingredients in step (h) to determine whether it meetsdesired performance requirements as part of a food preparation.
 2. Themethod of claim 1, wherein the target food function is selected fromantimicrobial activity, gelation, moisture retention, fat structuring,adhesion, fiber formation, and particular flavors.
 3. The method ofclaim 1, wherein the machine learning includes deep learning andhomology comparison of the individual proteins.
 4. The method of claim1, wherein the characteristics analyzed in the training in step (a) andthe applying in step (b) includes a homology comparison of amino acidsequences of the individual proteins.
 5. The method of claim 1, whereinthe structural characteristics analyzed in the training in step (a) andthe applying in step (b) further include a homology comparison ofthree-dimensional structure of each individual protein, obtained fromcrystallography data or predicted from the protein's amino acidsequence.
 6. The method of claim 1, wherein the properties analyzed inthe training in step (a) and the applying in step (b) further include ahomology comparison of vector representations of physicochemical andbiochemical properties of amino acids and clusters of amino acids ofeach individual protein.
 7. The method of claim 1, wherein theidentifying of protein candidates in step (c) and/or the selecting ofpotential food ingredients in step (g) also includes assessing whetherthe protein candidate or potential food ingredient has or is predictedto have one or more additional desirable functions or properties.
 8. Themethod of claim 7, wherein the other desirable functions or propertiesinclude one or more of the following: ease of expression, ease ofpurification, stability on storage, mixability, and one or moredesirable flavors or sensory properties.
 9. The method of claim 1,wherein the identifying of protein candidates in step (c) and/or theselecting of potential food ingredients in step (g) also includesremoving a protein as a protein candidate or potential food ingredientif it has or is predicted to have one or more undesirable functions orproperties.
 10. The method of claim 9, wherein the undesirable functionsor properties include one or more of the following: predictedallergenicity or immunogenicity, incompatibility with other foodingredients, an adverse physiological effect, and an undesirable flavor.11. The method of claim 1, further comprising selecting and removing oneor more individual proteins identified as species homologs and/orisoforms from the set of protein candidates before step (d).
 12. Themethod of claim 1, wherein step (d) is a high throughput expression andpurification process wherein each of a plurality of protein candidatesidentified in step (c) are expressed as a fusion protein also containingan amino acid tag sequence, and the protein candidates are purified byaffinity separation using a conjugate binding partner for the tagsequence before the assays are conducted in step (e).
 13. The method ofclaim 12, wherein the tag is left on protein candidates for conductingat least some of the assays of step (e), but removed from potential foodingredients for assessing whether they meet the desired performancerequirements in step (i).
 14. The method of claim 1, wherein theexpressing and purifying in step (d) and the assaying in step (e) arerepeated one or more times to improve volume and/or quality of proteinproduction.
 15. The method of claim 1, wherein the assaying in step (e)includes determining or measuring one or more physicochemical propertiesof the protein candidates selected from thermal stability, bufferingcapacity, solubility, and charge.
 16. The method of claim 1, wherein theassaying in step (e) includes determining or measuring one or morefunctional properties of the protein candidates selected from emulsionstability, foam stability, gelation, chewiness, storage modulus, waterbinding capacity, swell ratio in water, sedimentation rate,adhesiveness, antimicrobial activity, and enzyme activity.
 17. Themethod of claim 1, further comprising unmasking and testing anindividual protein that was predicted by the computer system in step (c)as having the target food function but was determined not to have thetarget food function when assayed in step (e), the unmasking comprising:recombinantly expressing and purifying a potentially unmasked version ofthe individual protein in which a part of the protein predicted to havethe target food function is excised from other parts of the protein thatare believed to mask the target food function, and then conductingadditional assays to determine or measure whether the potentiallyunmasked version of the protein has the target food function.
 18. Themethod of claim 1, wherein the performance requirements tested in step(i) include demonstration of the target food function by the potentialfood ingredient when compounded into a food product, and compliance ofthe food product with regulatory requirements.
 19. The method of claim2, wherein the target food function is gelation, wherein the individualprotein binds other ingredients at low concentrations and forms a gelwhen heated.
 20. The method of claim 2, wherein the target food functionis a particular flavor.
 21. The method of claim 19, wherein one or moreof the individual proteins selected as potential food ingredients instep (h) are assessed in step (i) as an egg replacement in a foodpreparation.
 22. The method of claim 1, further comprising: (j)manufacturing a food product in which a conventional food ingredienthaving said target food function is replaced with one or more individualproteins assessed in step (i) as meeting the desired performancerequirements.
 23. The method of claim 22, wherein the food product is avegan equivalent of a sausage or a meat patty.
 24. The method of claim22, wherein the food product is a baked good or a confectionary.